<<<<<<< HEAD ======= >>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Testing output of the coding.

<<<<<<< HEAD
## Loading the packages and setting adjustment
suppressMessages(source('function/libs.R'))
=======
## Loading the packages and setting adjustment
suppressMessages(source(paste0(getwd(),'/function/libs.R')))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091
  1. DT, 2. DT, 3. DT: #DT
## Read the datasets Refer to **Testing efficiency of coding.Rmd** at chunk
## `get-data-summary-table-2.1`
years <- seq(2011, 2015)

## Here I take the majority leagues setting profile which are 'league-10-12'
## fMYPriceB = Back with vigorish price; fMYPriceL = Lay with vigorish price
## Here we term as Fair Odds
lProfile <- c(AH = 0.1, OU = 0.12)

mbase <- readfirmDatasets(years = years) %>% arrfirmDatasets(., lProfile = lProfile)

## In order to analyse the AHOU, here I need to filter out all soccer matches
## other than AHOU. (For example : Corners, Total League Goals etc.)  the
## stakes amount display as $1 = $10,000
#'@ mbase$datasets[!(mbase$datasets$Home %in% mbase$corners)|!(mbase$datasets$Away %in% mbase$corners),]
dat <- mbase$datasets %>% filter((!Home %in% mbase$others) | (!Away %in% mbase$others)) %>% 
    mutate(Stakes = Stakes/10000, Return = Return/10000, PL = PL/10000, Month = month(ymd(Date), 
        label = TRUE))

#'@ pander(head(dat)) # exactly same layout with kable(x)
#'@ kable(head(dat)) ## example of the dataset in the research paper

<<<<<<< HEAD
dat %>% datatable(., caption = "Table 2.1.1 : Firm A Staking Data", extensions = c("ColReorder", 
    "ColVis", "TableTools"), options = list(dom = "TC<\"clear\">rlfrtip", colVis = list(exclude = c(0), 
    activate = "mouseover"), tableTools = list(sSwfPath = copySWF(pdf = TRUE)), 
    scrollX = TRUE, scrollCollapse = TRUE))

rm(years, readfirmDatasets, arrfirmDatasets)
rm(mbase)  ## We need to scrap the livescore data based on the raw data mbase without filter, but this is not the point in this research paper.

Please refer to Natural Language Analysis to see the firm A staking sample dataset.

summary(lm(Return ~ pHKRange, data = dat))
======= #'@ dat %>% datatable(.,caption='Table 2.1.1 : Firm A Staking Data',extensions=c('ColReorder','ColVis','TableTools'),options=list(dom='TC<'clear'>rlfrtip',colVis=list(exclude=c(0),activate='mouseover'),tableTools=list(sSwfPath=copySWF(pdf=TRUE)),scrollX=TRUE,scrollCollapse=TRUE)) rm(years, readfirmDatasets, arrfirmDatasets) rm(mbase) ## We need to scrap the livescore data based on the raw data mbase without filter, but this is not the point in this research paper.

Please refer to Natural Language Analysis to see the firm A staking sample dataset.

summary(lm(Return ~ pHKRange, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ pHKRange, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
<<<<<<< HEAD
 -63.00  -37.79  -17.25   12.27 2932.12 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)         62.000     79.381   0.781    0.435
pHKRange(0.2,0.3]  -41.836     97.222  -0.430    0.667
pHKRange(0.3,0.4]  -41.708     80.893  -0.516    0.606
pHKRange(0.4,0.5]  -38.831     79.551  -0.488    0.625
pHKRange(0.5,0.6]  -33.688     79.414  -0.424    0.671
pHKRange(0.6,0.7]  -32.608     79.390  -0.411    0.681
pHKRange(0.7,0.8]  -24.209     79.386  -0.305    0.760
pHKRange(0.8,0.9]   -2.116     79.386  -0.027    0.979
pHKRange(0.9,1]      1.002     79.386   0.013    0.990
pHKRange(1,1.1]     -8.396     79.387  -0.106    0.916
pHKRange(1.1,1.2]  -29.750     79.389  -0.375    0.708
pHKRange(1.2,1.3]  -40.504     79.394  -0.510    0.610
pHKRange(1.3,1.4]  -48.593     79.410  -0.612    0.541
pHKRange(1.4,1.5]  -50.162     79.447  -0.631    0.528
pHKRange(1.5,1.6]  -51.549     79.547  -0.648    0.517
pHKRange(1.6,1.7]  -51.870     79.758  -0.650    0.515
pHKRange(1.7,1.8]  -41.994     80.392  -0.522    0.601
pHKRange(1.8,1.9]  -49.856     81.088  -0.615    0.539
pHKRange(1.9,2]    -41.814     82.167  -0.509    0.611
pHKRange(2,2.1]    -62.000    112.262  -0.552    0.581
pHKRange(2.1,2.2]  -58.950     88.751  -0.664    0.507
pHKRange(2.2,2.3]  -17.440     86.958  -0.201    0.841
pHKRange(2.3,2.4]  -24.500     97.222  -0.252    0.801
pHKRange(2.4,2.5]  -56.000     84.862  -0.660    0.509
pHKRange(2.5,2.6]  -49.000     97.222  -0.504    0.614
pHKRange(2.6,2.7]   51.305    112.262   0.457    0.648
pHKRange(2.7,2.8]   68.500    112.262   0.610    0.542
pHKRange(2.8,2.9]  -62.000    112.262  -0.552    0.581
pHKRange(2.9,3]    -52.000    112.262  -0.463    0.643
pHKRange(3.3,3.4]  -62.000    112.262  -0.552    0.581
pHKRange(3.7,3.8]  -61.000    112.262  -0.543    0.587
pHKRange(3.8,3.9]  -62.000    112.262  -0.552    0.581

Residual standard error: 79.38 on 48608 degrees of freedom
Multiple R-squared:  0.03839,   Adjusted R-squared:  0.03778 
F-statistic:  62.6 on 31 and 48608 DF,  p-value: < 2.2e-16

DT

Table summary

summary(lm(Return ~ HCap, data = dat))
======= -62.95 -37.95 -17.20 12.28 2931.66 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 62.0000 79.3773 0.781 0.435 pHKRange(0.2,0.3] -41.8362 97.2170 -0.430 0.667 pHKRange(0.3,0.4] -40.4820 80.8339 -0.501 0.617 pHKRange(0.4,0.5] -38.7706 79.5405 -0.487 0.626 pHKRange(0.5,0.6] -33.9880 79.4089 -0.428 0.669 pHKRange(0.6,0.7] -32.4205 79.3859 -0.408 0.683 pHKRange(0.7,0.8] -24.0462 79.3824 -0.303 0.762 pHKRange(0.8,0.9] -1.6568 79.3821 -0.021 0.983 pHKRange(0.9,1] 0.9518 79.3819 0.012 0.990 pHKRange(1,1.1] -8.8444 79.3828 -0.111 0.911 pHKRange(1.1,1.2] -29.8912 79.3852 -0.377 0.707 pHKRange(1.2,1.3] -40.5030 79.3905 -0.510 0.610 pHKRange(1.3,1.4] -48.5522 79.4053 -0.611 0.541 pHKRange(1.4,1.5] -50.2087 79.4448 -0.632 0.527 pHKRange(1.5,1.6] -52.1420 79.5425 -0.656 0.512 pHKRange(1.6,1.7] -51.3715 79.7813 -0.644 0.520 pHKRange(1.7,1.8] -40.9934 80.4428 -0.510 0.610 pHKRange(1.8,1.9] -50.3617 81.0141 -0.622 0.534 pHKRange(1.9,2] -40.2615 82.3737 -0.489 0.625 pHKRange(2,2.1] -62.0000 112.2565 -0.552 0.581 pHKRange(2.1,2.2] -58.9500 88.7465 -0.664 0.507 pHKRange(2.2,2.3] -17.4400 86.9535 -0.201 0.841 pHKRange(2.3,2.4] -24.5000 97.2170 -0.252 0.801 pHKRange(2.4,2.5] -56.0000 84.8579 -0.660 0.509 pHKRange(2.5,2.6] -49.0000 97.2170 -0.504 0.614 pHKRange(2.6,2.7] 51.3050 112.2565 0.457 0.648 pHKRange(2.7,2.8] 68.5000 112.2565 0.610 0.542 pHKRange(2.8,2.9] -62.0000 112.2565 -0.552 0.581 pHKRange(2.9,3] -52.0000 112.2565 -0.463 0.643 pHKRange(3.3,3.4] -62.0000 112.2565 -0.552 0.581 pHKRange(3.7,3.8] -61.0000 112.2565 -0.543 0.587 pHKRange(3.8,3.9] -62.0000 112.2565 -0.552 0.581 Residual standard error: 79.38 on 48608 degrees of freedom Multiple R-squared: 0.03848, Adjusted R-squared: 0.03787 F-statistic: 62.75 on 31 and 48608 DF, p-value: < 2.2e-16

DT

Table summary

summary(lm(Return ~ HCap, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ HCap, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
 -62.48  -41.85  -24.78   13.29 2951.13 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  48.7248     0.4667  104.41   <2e-16 ***
HCap         -3.9289     0.2679  -14.67   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 80.75 on 48638 degrees of freedom
Multiple R-squared:  0.004403,  Adjusted R-squared:  0.004383 
F-statistic: 215.1 on 1 and 48638 DF,  p-value: < 2.2e-16

graph 3.4.1b linear model

<<<<<<< HEAD
summary(lm(Return ~ pHKRange, data = dat))
=======
summary(lm(Return ~ pHKRange, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ pHKRange, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
<<<<<<< HEAD
 -63.00  -37.79  -17.25   12.27 2932.12 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)         62.000     79.381   0.781    0.435
pHKRange(0.2,0.3]  -41.836     97.222  -0.430    0.667
pHKRange(0.3,0.4]  -41.708     80.893  -0.516    0.606
pHKRange(0.4,0.5]  -38.831     79.551  -0.488    0.625
pHKRange(0.5,0.6]  -33.688     79.414  -0.424    0.671
pHKRange(0.6,0.7]  -32.608     79.390  -0.411    0.681
pHKRange(0.7,0.8]  -24.209     79.386  -0.305    0.760
pHKRange(0.8,0.9]   -2.116     79.386  -0.027    0.979
pHKRange(0.9,1]      1.002     79.386   0.013    0.990
pHKRange(1,1.1]     -8.396     79.387  -0.106    0.916
pHKRange(1.1,1.2]  -29.750     79.389  -0.375    0.708
pHKRange(1.2,1.3]  -40.504     79.394  -0.510    0.610
pHKRange(1.3,1.4]  -48.593     79.410  -0.612    0.541
pHKRange(1.4,1.5]  -50.162     79.447  -0.631    0.528
pHKRange(1.5,1.6]  -51.549     79.547  -0.648    0.517
pHKRange(1.6,1.7]  -51.870     79.758  -0.650    0.515
pHKRange(1.7,1.8]  -41.994     80.392  -0.522    0.601
pHKRange(1.8,1.9]  -49.856     81.088  -0.615    0.539
pHKRange(1.9,2]    -41.814     82.167  -0.509    0.611
pHKRange(2,2.1]    -62.000    112.262  -0.552    0.581
pHKRange(2.1,2.2]  -58.950     88.751  -0.664    0.507
pHKRange(2.2,2.3]  -17.440     86.958  -0.201    0.841
pHKRange(2.3,2.4]  -24.500     97.222  -0.252    0.801
pHKRange(2.4,2.5]  -56.000     84.862  -0.660    0.509
pHKRange(2.5,2.6]  -49.000     97.222  -0.504    0.614
pHKRange(2.6,2.7]   51.305    112.262   0.457    0.648
pHKRange(2.7,2.8]   68.500    112.262   0.610    0.542
pHKRange(2.8,2.9]  -62.000    112.262  -0.552    0.581
pHKRange(2.9,3]    -52.000    112.262  -0.463    0.643
pHKRange(3.3,3.4]  -62.000    112.262  -0.552    0.581
pHKRange(3.7,3.8]  -61.000    112.262  -0.543    0.587
pHKRange(3.8,3.9]  -62.000    112.262  -0.552    0.581

Residual standard error: 79.38 on 48608 degrees of freedom
Multiple R-squared:  0.03839,   Adjusted R-squared:  0.03778 
F-statistic:  62.6 on 31 and 48608 DF,  p-value: < 2.2e-16

test

summary(lm(Return ~ HCap + HKPrice, data = dat))
======= -62.95 -37.95 -17.20 12.28 2931.66 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 62.0000 79.3773 0.781 0.435 pHKRange(0.2,0.3] -41.8362 97.2170 -0.430 0.667 pHKRange(0.3,0.4] -40.4820 80.8339 -0.501 0.617 pHKRange(0.4,0.5] -38.7706 79.5405 -0.487 0.626 pHKRange(0.5,0.6] -33.9880 79.4089 -0.428 0.669 pHKRange(0.6,0.7] -32.4205 79.3859 -0.408 0.683 pHKRange(0.7,0.8] -24.0462 79.3824 -0.303 0.762 pHKRange(0.8,0.9] -1.6568 79.3821 -0.021 0.983 pHKRange(0.9,1] 0.9518 79.3819 0.012 0.990 pHKRange(1,1.1] -8.8444 79.3828 -0.111 0.911 pHKRange(1.1,1.2] -29.8912 79.3852 -0.377 0.707 pHKRange(1.2,1.3] -40.5030 79.3905 -0.510 0.610 pHKRange(1.3,1.4] -48.5522 79.4053 -0.611 0.541 pHKRange(1.4,1.5] -50.2087 79.4448 -0.632 0.527 pHKRange(1.5,1.6] -52.1420 79.5425 -0.656 0.512 pHKRange(1.6,1.7] -51.3715 79.7813 -0.644 0.520 pHKRange(1.7,1.8] -40.9934 80.4428 -0.510 0.610 pHKRange(1.8,1.9] -50.3617 81.0141 -0.622 0.534 pHKRange(1.9,2] -40.2615 82.3737 -0.489 0.625 pHKRange(2,2.1] -62.0000 112.2565 -0.552 0.581 pHKRange(2.1,2.2] -58.9500 88.7465 -0.664 0.507 pHKRange(2.2,2.3] -17.4400 86.9535 -0.201 0.841 pHKRange(2.3,2.4] -24.5000 97.2170 -0.252 0.801 pHKRange(2.4,2.5] -56.0000 84.8579 -0.660 0.509 pHKRange(2.5,2.6] -49.0000 97.2170 -0.504 0.614 pHKRange(2.6,2.7] 51.3050 112.2565 0.457 0.648 pHKRange(2.7,2.8] 68.5000 112.2565 0.610 0.542 pHKRange(2.8,2.9] -62.0000 112.2565 -0.552 0.581 pHKRange(2.9,3] -52.0000 112.2565 -0.463 0.643 pHKRange(3.3,3.4] -62.0000 112.2565 -0.552 0.581 pHKRange(3.7,3.8] -61.0000 112.2565 -0.543 0.587 pHKRange(3.8,3.9] -62.0000 112.2565 -0.552 0.581 Residual standard error: 79.38 on 48608 degrees of freedom Multiple R-squared: 0.03848, Adjusted R-squared: 0.03787 F-statistic: 62.75 on 31 and 48608 DF, p-value: < 2.2e-16
summary(lm(Return ~ HCap + HKPrice, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ HCap + HKPrice, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
<<<<<<< HEAD
 -68.50  -41.85  -24.57   13.00 2949.97 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  66.1296     1.7065   38.75   <2e-16 ***
HCap         -4.1288     0.2682  -15.39   <2e-16 ***
HKPrice     -18.2079     1.7174  -10.60   <2e-16 ***
=======
 -68.49  -41.85  -24.57   12.99 2949.97 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  66.1051     1.7061   38.75   <2e-16 ***
HCap         -4.1284     0.2682  -15.39   <2e-16 ***
HKPrice     -18.1815     1.7168  -10.59   <2e-16 ***
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 80.65 on 48637 degrees of freedom
<<<<<<< HEAD
Multiple R-squared:  0.006699,  Adjusted R-squared:  0.006658 
F-statistic:   164 on 2 and 48637 DF,  p-value: < 2.2e-16
summary(lm(Return ~ ipRange, data = dat))
======= Multiple R-squared: 0.006694, Adjusted R-squared: 0.006653 F-statistic: 163.9 on 2 and 48637 DF, p-value: < 2.2e-16
summary(lm(Return ~ ipRange, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ ipRange, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
-116.54  -42.61  -25.31   13.49 2945.54 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     41.50801    1.27331  32.599  < 2e-16 ***
ipRange(10,15]   4.67793    2.31126   2.024 0.042979 *  
ipRange(15,20]   3.18778    2.32662   1.370 0.170651    
ipRange(20,25]   1.33495    2.31633   0.576 0.564402    
ipRange(25,30]  -2.49469    2.30219  -1.084 0.278540    
ipRange(30,35]   0.13349    2.31310   0.058 0.953979    
ipRange(35,40]   2.60197    2.41188   1.079 0.280676    
ipRange(40,45]   1.95195    2.34688   0.832 0.405570    
ipRange(45,50]   2.31935    2.47645   0.937 0.348988    
ipRange(5,10]    1.10243    1.96256   0.562 0.574303    
ipRange(50,55]   0.11829    2.29905   0.051 0.958966    
ipRange(55,60]  -0.98648    2.38947  -0.413 0.679723    
ipRange(60,65]   0.22325    2.68400   0.083 0.933712    
ipRange(65,70]   0.46061    2.93477   0.157 0.875286    
ipRange(70,75]  -0.48891    3.17289  -0.154 0.877541    
ipRange(75,80]  -0.05228    4.08034  -0.013 0.989778    
ipRange(80,85]   4.50263    7.23165   0.623 0.533532    
ipRange(85,90]  15.27115   26.98091   0.566 0.571397    
ipRangeET       75.03649   10.43012   7.194 6.37e-13 ***
ipRangeFT      -14.20101   20.91483  -0.679 0.497146    
ipRangeHT       12.05429    2.57128   4.688 2.77e-06 ***
ipRangeNo        4.94791    1.39572   3.545 0.000393 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 80.85 on 48618 degrees of freedom
Multiple R-squared:  0.002204,  Adjusted R-squared:  0.001773 
F-statistic: 5.115 on 21 and 48618 DF,  p-value: 1.434e-13
<<<<<<< HEAD
summary(lm(Return ~ CurScore + ipHCap, data = dat))
=======
summary(lm(Return ~ CurScore + ipHCap, data = dat))
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

Call:
lm(formula = Return ~ CurScore + ipHCap, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
<<<<<<< HEAD
-210.29  -41.25  -24.27   12.94 2942.06 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  40.4134     0.7420  54.468  < 2e-16 ***
CurScore0-1   3.2969     1.6715   1.972  0.04857 *  
CurScore0-2   1.9420     3.4739   0.559  0.57615    
CurScore0-3   9.9639     9.3611   1.064  0.28716    
CurScore0-4  10.6088    22.5450   0.471  0.63796    
CurScore0-5 -11.6487    45.0722  -0.258  0.79606    
CurScore1-0   2.3152     1.5680   1.477  0.13980    
CurScore1-1   4.9467     2.3974   2.063  0.03908 *  
CurScore1-2   3.5465     3.7904   0.936  0.34946    
CurScore1-3  -5.2166    10.3647  -0.503  0.61476    
CurScore1-4  -3.2119    21.6602  -0.148  0.88212    
CurScore2-0   3.1421     2.4630   1.276  0.20207    
CurScore2-1   6.3561     4.4060   1.443  0.14914    
CurScore2-2   7.7655     7.3160   1.061  0.28850    
CurScore2-3  12.9682    11.9258   1.087  0.27686    
CurScore2-4 -14.3164    45.0698  -0.318  0.75075    
CurScore3-0   1.2109     6.2150   0.195  0.84552    
CurScore3-1   7.4008     8.5488   0.866  0.38666    
CurScore3-2   2.8428    18.9448   0.150  0.88072    
CurScore3-3  22.8109    23.5450   0.969  0.33264    
CurScore3-4 227.6231    55.1949   4.124 3.73e-05 ***
CurScore4-0   0.3637    23.5465   0.015  0.98768    
CurScore4-1   3.1720    20.8733   0.152  0.87922    
CurScore4-2  -6.6857    29.5102  -0.227  0.82077    
CurScore4-3  43.7216    78.0544   0.560  0.57539    
CurScore5-0  -8.7580    24.7024  -0.355  0.72293    
CurScore5-1 -36.0754    78.0547  -0.462  0.64395    
CurScore5-2  -3.3488    55.1995  -0.061  0.95162    
CurScore5-3  18.3565    78.0562   0.235  0.81408    
CurScoreNo    3.7381     0.9939   3.761  0.00017 ***
ipHCap       -2.8920     0.2464 -11.739  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 78.05 on 35273 degrees of freedom
  (13336 observations deleted due to missingness)
Multiple R-squared:  0.005217,  Adjusted R-squared:  0.004371 
F-statistic: 6.166 on 30 and 35273 DF,  p-value: < 2.2e-16

graph 3.4.1c linear model

## Set options back to original options
options(op)

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======= -210.27 -42.55 -24.95 13.53 2941.20 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 41.1712 0.6622 62.176 < 2e-16 *** CurScore0-1 2.5703 1.5124 1.699 0.08923 . CurScore0-2 -1.1782 3.0474 -0.387 0.69903 CurScore0-3 8.4763 7.9125 1.071 0.28406 CurScore0-4 8.1098 22.4211 0.362 0.71758 CurScore0-5 -12.7071 46.6558 -0.272 0.78535 CurScore1-0 2.3791 1.4307 1.663 0.09635 . CurScore1-1 4.6754 1.9264 2.427 0.01522 * CurScore1-2 0.5902 3.4687 0.170 0.86489 CurScore1-3 -8.4706 9.9671 -0.850 0.39541 CurScore1-4 -7.4131 21.6036 -0.343 0.73149 CurScore2-0 2.0544 2.3877 0.860 0.38957 CurScore2-1 9.0203 3.3781 2.670 0.00758 ** CurScore2-2 7.0654 5.2787 1.338 0.18075 CurScore2-3 11.6166 10.6295 1.093 0.27446 CurScore2-4 8.4748 36.1393 0.235 0.81459 CurScore3-0 2.7589 5.9009 0.468 0.64012 CurScore3-1 7.2699 7.5309 0.965 0.33438 CurScore3-2 1.4799 10.6308 0.139 0.88929 CurScore3-3 -3.2432 16.1726 -0.201 0.84106 CurScore3-4 226.8492 57.1347 3.970 7.18e-05 *** CurScore4-0 11.1241 22.4222 0.496 0.61981 CurScore4-1 4.1359 17.2413 0.240 0.81042 CurScore4-2 -11.5186 23.3330 -0.494 0.62155 CurScore4-3 2.0130 57.1346 0.035 0.97189 CurScore5-0 -9.8734 25.5684 -0.386 0.69938 CurScore5-1 -37.0265 80.7989 -0.458 0.64677 CurScore5-2 -4.4771 57.1395 -0.078 0.93755 CurScore5-3 17.9208 80.7999 0.222 0.82448 CurScoreNo 4.0981 0.8759 4.679 2.89e-06 *** ipHCap -2.7632 0.2447 -11.293 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 80.8 on 48609 degrees of freedom Multiple R-squared: 0.003804, Adjusted R-squared: 0.003189 F-statistic: 6.188 on 30 and 48609 DF, p-value: < 2.2e-16

graph 3.4.1c linear model

## Set options back to original options
options(op)
>>>>>>> 8c8a740b2768e7c450af85ace180489785115091